Thinking

Glad to see people asking good questions and taking a critical look at data, its gathering process, its analysis and the way it’s correlated loosely to arrive at theoretical conclusions that are often treated as facts.

Can an algorithm be racist? It’s a question that should be of concern for all data-driven organizations.

From analytics that help law enforcement predict future crimes, to retailers assessing the likelihood of female customers being pregnant (in the case of Target, without their knowledge), the increasing scale of computer cognizance is raising difficult ethical questions for business.

Witness the controversy that the crime app SketchFactor caused in launching its crowdsourced service in the US. The app works by allowing users to report, in real time, how subjectively “sketchy” a particular neighborhood may be, enabling an algorithm to determine the apparent safety of the area for pedestrians. Inevitably, the app has drawn accusations of racism, with some commentators labeling it a service that literally color-codes neighborhoods.

Of course, marketers have always targeted racially defined customer-bases—typically to adjust price ranges along socio-economic lines. But with ever more data becoming available, the risk…

There’s simply nothing you should just accept at face value. You should always do your own homework.

Critical thinkers rarely take something they read or hear as fact. Critical thinkers will seek out other perspectives. The reflex action of a critical thinker is to assume there’s another side to whatever story they’re told.

What alarms me is how often you see people take a premise at face value with no questions.

There was a time in the late 60s and early 70s when the phrase “question authority” was a well-known and popular refrain.

Fast forward to 2015, and we’re getting emails from the current administration’s “Truth Team.” (The official organization is the “Organizing for Action Truth Team.”)

As a marketer, I would have cautioned any politician from having a self-described “truth team.” It just screams 1984 and Russian and German propaganda, to me.

Let me tell you a story. Today during lunch I did what I always do, I read an article by people who are supposed to be much smarter than I am. Surprisingly what I read explained my interactions with other people, especially when it comes to their disdain for data and preference for personal stories.

As I processed this article, I began to realize that there is a biological reason for why we prefer to believe the anecdotes our friends tell rather than cold, hard, facts. It turns out we humans are hardwired to prefer narrative.

Apparently a bunch of really smart scientist-people at Emory University did some tests and they discovered that hearing a story releases a chemical called oxytocin (don’t get excited, that’s different than oxycodone) and as it happens, this is the chemical released by breastfeeding mothers that illicits bonding.

The last round of brainstorming tightened up my thinking around the methodology. Like all engineering endeavors I needed to identify the beginning point for creating the enterprise. Most of the business model approaches are just that. They document the existing and while that is necessary, is that sufficient? When I’ve attended workshops focused on business models, often it ends up around optimization rather than innovation. The strategy and planning methodology I devised and fielded for other corporations was purposely limited, because at that time these corporation’s business models were deemed untouchable. Even though several people could see its useful life was coming to an end. Secondly, reengineering a business model it difficult for most in management positions…their mentality is around optimizing the status quo. Not a blame, it how they were successful and how they are managed in most corporations today.

So I was slurping up cereal over my keyboard, figuring that Lucky Charms on your keyboard are the new power breakfast, when my daughter’s friend Fiona Doherty appeared in my office waving an article from the New York Times that she thought I might find interesting.

Even though a raft of scientific studies have conclusively proven that multitasking can damage your career and your brain, I started skimming the article while answering emails because Fiona is a fabulous person and I did not want to be rude. But after about five seconds, I stopped multitasking and just read the article full-out.

Introduction – uncertainty and decision-making

Managing uncertainty -deciding what to do in the absence of reliable information – is a significant part of project management and many other managerial roles. When put this way, it is clear that managing uncertainty is primarilya decision-making problem. Indeed, as I will discuss shortly, the main difficulties associated with decision-making are related to specific types of uncertainties that we tend to overlook.

Let’s begin by looking at the standard approach to decision-making, which goes as follows:

Define the decision problem.

Identify options.

Develop criteria for rating options.

Evaluate options against criteria.

Select the top rated option.

As I have pointed out in this post, the above process is too simplistic for some of the complex, multifaceted decisions that we face in life and at work (switching jobs, buying a house or starting a business venture, for example). In such cases: